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ANLI Benchmark (Natural Language Inference)
Natural language inference (NLI) is the task of determining whether a "hypothesis" is true (entailment), false (contradiction), or undetermined (neutral) given a "premise".
Example:
| Premise | Label | Hypothesis |
|---|---|---|
| A man inspects the uniform of a figure in some East Asian country. | contradiction | The man is sleeping. |
| An older and younger man smiling. | neutral | Two men are smiling and laughing at the cats playing on the floor. |
| A soccer game with multiple males playing. | entailment | Some men are playing a sport. |
Approaches used for NLI include earlier symbolic and statistical approaches to more recent deep learning approaches. Benchmark datasets used for NLI include SNLI, MultiNLI, SciTail, among others. You can get hands-on practice on the SNLI task by following this d2l.ai chapter.
Further readings:
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Results archive 2025-07-28
No rows in the archive for this table at snapshot 2025-07-28. It declares 11 metrics (Accuracy, F1 Macro, F1 Micro, F1 Weighted, Precision Macro, Precision Micro, Precision Weighted, Recall Macro, Recall Micro, Recall Weighted, loss) but no result was ever recorded against it. That says nothing about whether results exist elsewhere.
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